Analysis on the related factors of China's technological innovation ability using greyness relational degree

PurposeIn order to make grey relational analysis applicable to the interval grey number, this paper discusses the model of grey relational degree of the interval grey number and uses it to analyze the related factors of China's technological innovation ability.Design/methodology/approachFirst, this paper gives the definitions of the lower bound domain, the value domain, the upper bound domain of interval grey number and the generalized measure and the generalized greyness of interval grey number. Then, based on the grey relational theory, this paper proposes the model of greyness relational degree of the interval grey number and analyzes its relationship with the classical grey relational degree. Finally, the model of greyness relational degree is applied to analyze the related factors of China's technological innovation ability.FindingsThe results show that the model of greyness relational degree has strict theoretical basis, convenient calculation and easy programming and can be applied to the grey number sequence, real number sequence and grey number and real number coexisting sequence. The relational order of the four related factors of China's technological innovation ability is research and development (R&D) expenditure, R&D personnel, university student number and public library number, and it is in line with the reality.Practical implicationsThe results show that the sequence values of greyness relational degree have large discreteness, and it is feasible and effective to analyze the related factors of China's technological innovation ability.Originality/valueThe paper succeeds in realizing both the model of greyness relational degree of interval grey number with unvalued information distribution and the order of related factors of China's technological innovation ability.

Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights

Tail-welded blanks (TWBs) are widely used in automotive bodies to improve structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (312) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method; the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.

Grey Correlation Analysis of Economic Growth and Cultural Industry Competitiveness

The influence of cultural industry competitiveness on economic growth is analyzed by using grey relational degree method. Then, the influence of cultural industry on the three industries is analyzed and compared in the same way. On this basis, further from the cultural industry, the impacts of core layer, outer layer, and related layer on economic growth were compared and analyzed. Finally, the economic growth model is used to measure the impact of investment, labor, and innovation in cultural industry on economic growth. The results show that cultural industry has a great influence on economic growth. The cultural industry has an obvious driving effect on the tertiary industry. The correlative layer of cultural industry has the greatest influence on economic growth. Cultural industry innovation has a huge pulling effect on economic growth. The SPSS software was used to process the data, and the data indicators were screened. Finally, the grey relational degree model was constructed. Then, the improved diamond model was used to select the influential factors of the development of cultural industry, and the main influencing factors were found out through the grey relational degree analysis. This paper analyzes the factors affecting economic benefits and puts forward countermeasures. The results show that it is of great significance to pay attention to the development of cultural manufacturing industry in cultural industry and promote cultural innovation to promote economic growth.

Fuzzy Clustering Method Based on Improved Weighted Distance

As an essential data processing technology, cluster analysis has been widely used in various fields. In clustering, it is necessary to select appropriate measures to evaluate the similarity in the data. In this paper, firstly, a cluster center selection method based on the grey relational degree is proposed to solve the problem of sensitivity in initial cluster center selection. Secondly, combining the advantages of Euclidean distance, DTW distance, and SPDTW distance, a weighted distance measurement based on three kinds of reach is proposed. Then, it is applied to Fuzzy C-MeDOIDS and Fuzzy C-means hybrid clustering technology. Numerical experiments are carried out with the UCI datasets. The experimental results show that the accuracy of the clustering results is significantly improved by using the clustering method proposed in this paper. Besides, the method proposed in this paper is applied to the MUSIC INTO EMOTIONS and YEAST datasets. The clustering results show that the algorithm proposed in this paper can also achieve a better clustering effect when dealing with practical problems.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.